sort_values() by single column in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the DataFrame by the 'age' column in ascending order.

Pandas

sorted_df = df.sort_values(by=[1])

Drag options to blanks, or click blank then click option'

A'name'

B'height'

C'score'

D'age'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the column name in quotes.

Using a column name that does not exist in the DataFrame.

The sort_values() function sorts the DataFrame by the column name given in the by parameter. Here, we want to sort by the 'age' column.

2fill in blank

medium

Complete the code to sort the DataFrame by the 'score' column in descending order.

Pandas

sorted_df = df.sort_values(by='score', ascending=[1])

Drag options to blanks, or click blank then click option'

AFalse

BTrue

C'False'

D'True'

Attempts:

3 left

💡 Hint

Common Mistakes

Using strings 'True' or 'False' instead of boolean True or False.

Setting ascending to True which sorts in ascending order.

Setting ascending=False sorts the DataFrame in descending order.

3fill in blank

hard

Fix the error in the code to correctly sort the DataFrame by the 'height' column.

Pandas

sorted_df = df.sort_values(by=[1])

Drag options to blanks, or click blank then click option'

A'height'

Bheight

Cdf['height']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes causing a NameError.

Passing the entire column series instead of the column name.

The by parameter requires the column name as a string, so it must be in quotes.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length, but only for words longer than 4 characters.

Pandas

{word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Blen(word) > 4

Cword > 4

Dword.length()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself in the condition instead of its length.

Using incorrect syntax like word.length() which is not valid in Python.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 4 using len(word) > 4.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase word to its score, but only if the score is positive.

Pandas

{ [1]: [2] for [3], [2] in data.items() if [2] > 0 }

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using item instead of unpacking key and value.

Not converting the key to uppercase.

Using the wrong variable names in the comprehension.

The comprehension maps the uppercase version of the key (k.upper()) to its value (v) for each key-value pair (k, v) in data.items(), filtering only positive values.

Practice

1. What does the sort_values() function do in pandas when used on a DataFrame?

easy

A. It filters rows based on a condition.

B. It changes the data type of a column.

C. It sorts the DataFrame rows based on the values in a specified column.

D. It adds a new column to the DataFrame.

Solution

1. Step 1: Understand the purpose of sort_values()

   This function is used to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other options

   Changing data types, filtering rows, or adding columns are done by different functions, not sort_values().

3. Final Answer:

   It sorts the DataFrame rows based on the values in a specified column. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column [OK]

Hint: Remember: sort_values() orders rows by column values [OK]

Common Mistakes:

• Confusing sorting with filtering
• Thinking it changes data types
• Assuming it adds columns

2. Which of the following is the correct syntax to sort a DataFrame df by the column 'age' in ascending order?

easy

A. df.sort_values(by='age', ascending=True)

B. df.sort_values('age', ascending=False)

C. df.sort(by='age', ascending=True)

D. df.sort_values(by='age', ascending='yes')

Solution

1. Step 1: Check the correct function and parameters

   The function is sort_values() and it requires the parameter by='column_name'. The ascending parameter should be a boolean.

2. Step 2: Validate each option

   df.sort_values(by='age', ascending=True) uses correct function and parameters. df.sort_values('age', ascending=False) has ascending=False (descending). df.sort(by='age', ascending=True) uses wrong function sort(). df.sort_values(by='age', ascending='yes') uses a string instead of boolean for ascending.

3. Final Answer:

   df.sort_values(by='age', ascending=True) -> Option A

4. Quick Check:

   Correct syntax = df.sort_values(by='age', ascending=True) [OK]

Hint: Use by='column' and ascending=True/False in sort_values() [OK]

Common Mistakes:

• Using sort() instead of sort_values()
• Passing ascending as string instead of boolean
• Omitting 'by' keyword

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara'], 'score': [88, 92, 85]})
sorted_df = df.sort_values(by='score')

What is the order of names in sorted_df?

medium

A. ['Cara', 'Anna', 'Bob']

B. ['Bob', 'Anna', 'Cara']

C. ['Anna', 'Bob', 'Cara']

D. ['Cara', 'Bob', 'Anna']

Solution

1. Step 1: Understand sorting by 'score'

   The scores are 88 (Anna), 92 (Bob), and 85 (Cara). Sorting ascending means smallest to largest.

2. Step 2: Order names by ascending scores

   Sorted scores: 85 (Cara), 88 (Anna), 92 (Bob). So names order is ['Cara', 'Anna', 'Bob'].

3. Final Answer:

   ['Cara', 'Anna', 'Bob'] -> Option A

4. Quick Check:

   Ascending sort by score = ['Cara', 'Anna', 'Bob'] [OK]

Hint: Sort ascending means smallest value first [OK]

Common Mistakes:

• Assuming descending order by default
• Mixing up names and scores
• Not using 'by' parameter

4. What is wrong with this code snippet?

df = pd.DataFrame({'A': [3, 1, 2]})
df.sort_values('A', ascending='False')

medium

A. The column name should be passed as a list.

B. The ascending parameter should be a boolean, not a string.

C. sort_values() does not accept the ascending parameter.

D. The DataFrame must be sorted inplace.

Solution

1. Step 1: Check the ascending parameter type

   The ascending parameter expects a boolean (True or False), but here it is passed as a string 'False'.

2. Step 2: Validate other options

   Passing column name as string is allowed. sort_values() accepts ascending. Sorting inplace is optional.

3. Final Answer:

   The ascending parameter should be a boolean, not a string. -> Option B

4. Quick Check:

   ascending must be True/False boolean [OK]

Hint: Use True/False, not 'True'/'False' for ascending [OK]

Common Mistakes:

• Passing ascending as string
• Thinking column name must be list
• Forcing inplace sorting unnecessarily

5. You have a DataFrame df with columns 'product' and 'price'. You want to sort the products by price descending but keep the original DataFrame unchanged. Which code achieves this?

hard

A. df.sort_values(by='price', ascending=False, inplace=True)

B. df.sort_values('price', inplace=False)

C. df.sort_values(by='price', ascending=True)

D. sorted_df = df.sort_values(by='price', ascending=False)

Solution

1. Step 1: Understand sorting without changing original

   To keep original DataFrame unchanged, do not use inplace=True. Instead, assign the sorted result to a new variable.

2. Step 2: Check options for descending order

   sorted_df = df.sort_values(by='price', ascending=False) sorts by 'price' descending and assigns to sorted_df. df.sort_values(by='price', ascending=False, inplace=True) changes original due to inplace=True. df.sort_values(by='price', ascending=True) sorts ascending, not descending. df.sort_values('price', inplace=False) sorts ascending by default and does not assign result.

3. Final Answer:

   sorted_df = df.sort_values(by='price', ascending=False) -> Option D

4. Quick Check:

   Assign sorted result without inplace for original unchanged [OK]

Hint: Assign sorted DataFrame to new variable, avoid inplace=True [OK]

Common Mistakes:

• Using inplace=True and losing original order
• Sorting ascending instead of descending
• Not assigning sorted DataFrame to a variable